Simplified texture comparison engine

ABSTRACT

A method for calculating a coating textures indicator can comprise receiving target coating texture variables from an image. The method can also comprise accessing a relative texture characteristic database that stores a set of texture characteristic relationships for a plurality of coatings. The method can further comprise calculating a correlation between the target coating texture variables and target coating texture variables associated with a compared coating. Based upon the calculated correlation, the method can comprise, calculating a set of relative texture characteristics for the target coating that indicate relative differences in texture between the target coating and the compared coating. Each of the relative texture characteristics can comprise an assessment over all angles of the target coating.

BACKGROUND OF THE INVENTION

Coatings have been used for hundreds of years for protection and to addvisual appeal to products and structures. For example, houses arepainted or stained in order to protect the underlying siding from theweather and also to add aesthetic qualities to the house. Similarly,automobiles are painted, sometimes with multiple purpose-made layers, toprotect the metal body of the vehicle and also to add visual appeal tothe vehicle.

Various coatings may have specific features and properties that arebeneficial or desirable for certain uses. For example, differentcoatings can have different electrical conductive properties, differentchemical reactivity properties, different hardness properties, differentUV properties, and other different use-specific properties.Additionally, coatings may comprise unique visual features. For example,some automotive coatings comprise texture features that give the coatingunique visual effects.

The ability to provide highly consistent coating compositions is animportant aspect in many different coating markets. For example, it isdesirable for decorative coatings to comprise consistent colors andvisual features. Similarly, the ability to match previously appliedcoatings to available coating colors is important. For example, whenfixing a scratch in a car's coating, it is desirable to match both thecolor and the texture of the original coating. The ability to matchcoatings requires both consistent coating compositions and tools forcorrectly identifying the target coating and/or identifying anacceptable composition to match the target coating.

Significant technical difficulties exist in providing complex coatingand texture information to end users. For example, coating informationinvolves large numbers of distinct measurements from different angles.The resulting datasets can be large and difficult to use in practice. Assuch, there is a need for technically sound methods and schemes forprocessing large coating datasets and presenting the resultinginformation to end users in consistent terms that are easy to use andunderstand.

BRIEF SUMMARY OF THE INVENTION

The present invention comprises, among other things,computer-implemented methods, systems, and computer-program products forcalculating a coating texture indicator.

For example, one method for calculating a coating textures indicatorcomprises receiving target coating texture variables from an image of atarget coating. The method also comprises accessing a relative texturecharacteristic database that stores a set of texture characteristicrelationships for a plurality of coatings. The method further comprisescalculating a correlation between the target coating texture variablesand bulk texture data variables associated with a compared coating. Themethod still further comprises, based upon the calculated correlation,calculating a set of relative texture characteristics for the targetcoating that indicate relative differences in texture between the targetcoating and the compared coating. The relative texture characteristics,in turn, comprise an assessment over all angles of the target coating.

Additionally, the present invention comprises a computerized systemconfigured to perform a method for calculating coating textureindicators. In one exemplary implementation, for example, the systemreceives target coating texture variables, which can comprise bulktexture data variables generated from an image of a target coating. Thesystem also identifies, based upon information received from thecamera-enabled spectrophotometer, a coating color associated with atarget coating. In addition, the system accesses a relative texturecharacteristic database, which comprises a set of relative texturecharacteristics for one or more coatings that are related to the coatingcolor. Furthermore, the system calculates a correlation between thetarget coating texture variables and bulk texture data variablesassociated with the proposed matched coating. Still further, based uponthe calculated correlation, the system calculates a set of relativetexture characteristics for the proposed matched coating that indicaterelative differences in texture between the target coating and theproposed matched coating. Each of the relative texture characteristicscan comprise an assessment over all angles of the target coating.Further still, implemented method can include transmitting digital datacapable of causing a display to depict the set of relative texturecharacteristics.

Further, the present invention comprises a method for calculating acoating textures indicator. In this exemplary implementation, the methodcomprises receiving target coating texture variables from an image of atarget coating, which can comprise bulk texture data variables generatedfrom the image. The method also comprises accessing a relative texturecharacteristic database, which comprises a set of texture characteristicrelationships for a plurality of coatings. Additionally, the methodcomprises calculating a correlation between the target coating texturevariables and bulk texture data variables associated with a plurality ofdifferent coatings. Still further, the method comprises, based upon thecalculated correlation, calculating a set of relative texturecharacteristics for the target coating that indicate relativedifferences in texture between the target coating and the plurality ofdifferent coatings, wherein each of the relative texture characteristicscomprises an assessment over all angles of the target coating. Themethod also comprises transmitting digital data capable of causing adisplay to depict the set of relative texture characteristics.

Additional features and advantages of exemplary implementations of theinvention will be set forth in the description which follows, and inpart will be obvious from the description, or may be learned by thepractice of such exemplary implementations. The features and advantagesof such implementations may be realized and obtained by means of theinstruments and combinations particularly pointed out in the appendedclaims. These and other features will become more fully apparent fromthe following description and appended claims, or may be learned by thepractice of such exemplary implementations as set forth hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above recited and otheradvantages and features of the invention can be obtained, a moreparticular description of the invention briefly described above will berendered in the following by reference to the appended drawings.Understanding that these drawings depict only exemplary or typicalimplementations of the invention and are not therefore to be consideredto be limiting of its scope, the invention will be described andexplained with additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 depicts a schematic diagram of a system for calculating a coatingtexture in accordance with implementations of the present invention;

FIG. 2A depicts a relative texture characteristic chart and accompanyingexample coatings in accordance with implementations of the presentinvention;

FIG. 2B depicts another relative texture characteristic chart andaccompanying example coatings in accordance with implementations of thepresent invention;

FIG. 2C depicts yet another relative texture characteristic chart andaccompanying example coatings in accordance with implementations of thepresent invention;

FIG. 3 depicts a line chart of relative texture characteristics inaccordance with implementations of the present invention;

FIG. 4 depicts a table of texture variables in accordance withimplementations of the present invention;

FIG. 5 depicts a graph of a correlation of perceived texturecharacteristics in accordance with implementations of the presentinvention; and

FIG. 6 depicts a flow chart of steps within a method for calculating atexture characteristic in accordance with implementations of the presentinvention.

DETAILED DESCRIPTION

The present invention extends to methods and systems configured tocharacterize a target coating with respect to one or more previouslyanalyzed reference coatings. Herein, computer systems and dataacquisition devices may be used to gather texture information from atarget coating and generate one or more texture outputs that describethe target coating relative to one or more other coatings. The presentinvention may employ computer systems and data acquisition devices forreceiving large data sets of texture variables and transforming thelarge dataset into simplified and readily useable texture valueindicators. Further, the present invention may also comprise datatransformations for mapping unprocessed texture variables tohuman-perceived texture characteristics. Implementations of the presentinvention provide novel and non-obvious improvements to the field ofcoating matching.

Accordingly, the present invention provides novel and innovative systemsand methods for analyzing and matching a wide range of coating texturesin a highly reliable way using human-understandable qualitative values(e.g., “coarseness,” “sparkle,” “intensity,” etc.) Thus, in contrast toconventional methods of displaying texture differences with few orotherwise difficult to understand characteristics, the present inventioncan provide a wide range of simple and clear information that isunderstandable by a lay person. Additionally, the present invention canprovide a true visual texture match for an analyzed coating. Inparticular, according to the present invention a target coating may bematched to reference data that is based upon visual impressions(typically human impressions) of a large cross-section of the generalpopulation. As such, the present invention can provide a simpler andmore accurate means for analyzing and matching coating texture, andhence for an end user to make meaningful coating selections.

FIG. 1 depicts a schematic diagram of a system 105 configured for use incalculating a coating texture in accordance with one or more aspects ofthe present invention. As understood herein, system 105 can comprise anynumber or combination of computing devices 160, such as the depicteddesktop computer system, and/or any one or more laptop computers, mobiledevices, and personal device assistants (PDAs), as well as more complexsystems including remote data or application servers (not shown). FIG. 1further shows that system 105 can include one or more coatinginformation databases 140. As understood more fully herein, coatinginformation databases 140 can include a wide variety of informationrelevant to various coating characteristics, and human-perceptions ofthe coating characteristics, and can include data related to one or more“relative texture characteristic databases.” FIG. 1 further shows thatthe one or more coating information databases 140 may be integratedwithin system 105, such as a data structure of coating texturecalculation software application 100, or may be located within apurpose-built database that is located locally or remotely to thecoating texture software application 100. One will appreciate that thesoftware application program(s) 100, in turn, can comprise any number ofcomputer-executable modules configured to execute the steps andfunctions described herein.

For purposes of this specification and claims, a “module” means a set ofone or more logical, computer-executable functions and/or one or morecomputer application sub-routines, which are executed within the coatingtexture calculation software application 100. Whereas, a “component” isunderstood herein as a physical component that is used within thecomputer system 105, such as camera-enabled spectrophotometer 110. Inaddition, a “database” means a computer-based data structure, including,for example, linked lists, hash maps, SQL databases, and otherorganized-data. One of ordinary skill in the art will readily appreciateand understand that the above definitions for modules and components aresomewhat arbitrary and that one having skill in the art will be able todiscern the scope and purpose of each respective module and componentrecited herein.

Returning to FIG. 1, in at least one embodiment, data input module 120can be configured to receive from camera-enabled spectrophotometer 110(or, herein, “spectrophotometer 110”) certain “target coating texturevariables” and coating color variables from an image taken from a targetcoating (not shown). As understood herein, a “target coating” generallymeans a coating, such as a paint, gloss, stain, or other appliedtreatment after application to a target object (not shown), such as adevice, automobile, wall, etc. However, a target coating is not solimited, and in some implementations may include primarily unappliedsolutions.

In addition, “target coating texture variables” mean raw data receivedfrom a target coating that reflect unique attributes of the targetcoating, which in turn is based at least in part on the underlingtexture elements (e.g., aluminum flakes, crushed mica, pearl, etc.)within the coating mixture. The texture elements combine with thecoating to create corresponding texture effects that are reflected inthe spectrophotometer data, and thus create a unique profile orsignature from one coating to the next. Thus, by way of explanation, the“variable” aspect of this term generally owes to the notion that thereceived texture data taken by spectrophotometer 110 from the coatedobject will be generally consistent within the same coating with thesame texture elements, but will “vary” at certain points in the datawith coatings (same or different coatings) with different textureelements. Thus, as understood more fully herein, target coating texturevariables are in essence a texture “signature” for the texture elementswithin in a particular coating of a company's coating collection.

Although one will readily appreciate that different camera-enabledspectrophotometers (e.g., brands thereof) will relay different raw dataand different variables as an output for any particular coating texture,the raw data between different camera-enabled spectrophotometers canstill be processed in accordance with the present invention to provide aunique “signature” (or set of “target coating texture variables”) fromone coating to the next. This will generally be the case when a givencamera-enabled spectrophotometer comprises an internally consistentscheme for measuring texture variables from a target coating. Hence, onewill appreciate as understood more fully herein in context of thepresent invention, the exact value of the data taken from any givenspectrophotometer is not ordinarily as relevant as the variances in thedata that are consistent among coatings with particular types of textureelements.

One will appreciate that the coating data can be obtained a number ofdifferent ways through spectrophotometer 110. In some embodiments, forexample, the target coating texture variables may be derived from aphotograph of a coating (taken from a camera without aspectrophotometer) that is analyzed within the coating texturecalculated software application 100. In other embodiments, the targetcoating texture variables may be directly received from a camera-enabledspectrophotometer. In various embodiments, and as described above, theactual target coating texture variables received from thespectrophotometer 110 may depend upon the brand and type ofcamera-enabled spectrophotometer. For example, the actual target coatingtexture variables received from the camera-enabled spectrophotometer maycomprise proprietary manipulations and outputs, such that the number ofvariables and measured texture attributes that are represented by eachvariable are uncertain to anyone but the producer of the specificcamera-enabled spectrophotometer. Nevertheless, the data obtained fromany given brand of spectrophotometer 110 will provide a unique texturesignature for the analyzed coating texture.

FIG. 1 shows that software application 100 is in communication withcamera-enabled spectrophotometer 110 via data input module 120. Toaccomplish this communication, one will appreciate that the data inputmodule 120 can be in communication with (or otherwise implement) one ormore application program interfaces (“API(s)”) (not shown) that enablethe software application 100 to receive input into and/or send outputout of the coating texture calculating software application 100. Thiscommunication between data input module 120 and spectrophotometer 110(and any intervening APIs) can be accomplished via any physical orwireless connection means and corresponding communication interfacelayers. In any case, camera-enabled spectrophotometer 110 provides thesoftware application 100 with a set of target coating texture variablesfor a target coating.

In alternate implementations, the data input module 120 may directlyreceive an image of a coating. For example, one or more computingdevices 160 on which software application 100 is installed may furthercomprise a link to one or more cameras (not shown), and may enable thecomputing devices 160 to receive a photograph. One will appreciate that,in at least one implementation, software application 100 is incommunication with a camera configured to take photographs with at leastthree-times optical zoom.

However the image data is received, the data input module 120 can beconfigured to analyze the image of the coating and calculate desiredtexture variables. For example, data input module 120 may utilize ablack-and-white image taken from a coating sample to calculate the setof texture variables for the target coating because calculations can besimplified by removing color information. In other words, in at leastone implementation, data input module 120 may be configured to firststrip color from the received image file before calculation. Incontrast, in at least one implementation, a color image can be used tocalculate the set of texture variables for the target coating becauseadditional texture information may be available in a color image thatwould not otherwise be accessible in a black-and-white image.

FIG. 1 further shows that data input module 120 can provide the coatingcolor variables to a color match module 170. The color coating variablescan be received from a spectrophotometer and processed usingconventional methods. Using the coating color variables, the color matchmodule 170 can search a coating information database 140 for one or morecolors that most closely match the color of the target coating. In atleast one implementation, each of the colors stored within the coatinginformation database 140 can be associated with a particular coating andwith coating texture variables. For example, the color match module 170may determine that the target coating comprises a forest green colorthat is similar to a particular group of colors stored within thecoating information database 140.

Once one or more proposed matching colors have been identified, thecolor match module 170 can provide the texture calculating module 130with one or more indicators (not shown) of the proposed matches. Theindicators can comprise pointers to the proposed matches within thecoating information database, to data structures comprising informationabout each proposed match, or to any other data communication thatprovides the texture calculating module 130 with access to the necessarycoating information for the proposed matches.

As shown in FIG. 1, the texture calculation module 130 can then access,from within the coating information database 140, the coating texturevariables that are associated with each of the one or more proposedmatching coatings. Using the coating texture variables associated withthe proposed matching coatings and the coating texture variablesassociated with the target coating, the texture calculation module 130can calculate a correlation between the target coating and each of theproposed matching coatings. Based upon the calculated correlation, thetexture calculation module 130 can calculate a set of relative texturecharacteristics for the proposed matching coating that indicate relativedifferences in texture between the proposed matching coating and thetarget coating. Each of the relative texture characteristics cancomprise an assessment over all angles of the target coating.

In one implementation, the relative texture characteristics can be basedon human-provided relative visual impressions between differentreference coatings. For example, the relative visual impressions cancomprise human generated values pertaining to a relative coarseness, arelative sparkle intensity, and/or a relative sparkle density withrespect to a plurality of different reference coatings. In oneimplementation, the relative impressions can be gathered from a largegroup of diverse individuals that have viewed several different coatingsamples with respect to each other. In such a case, the data may reflectimpressions by the individuals as to various texture characteristics ofthe samples.

For instance, in at least one implementation, an individual can beprompted to rate the respective samples as having relatively more orless overall texture on a numeric scale. Similarly, Individuals can ratethe respective samples on a relative scale with respect to coarseness,sparkle intensity, and/or sparkle density. The relative impressions canthen be statistically mapped to coating texture variables that areassociated with each of the respective samples. Accordingly, astatistical correlation can be created between each of the coatingtexture variables received from the spectrophotometer and the humanperception of various texture characteristics.

The texture calculation module 130 can utilize the statisticalcorrelation of texture variables to identify a set of relative texturecharacteristics of the target coating (not shown) with respect to eachof the proposed coating matches. For example, the texture calculationmodule 130 can calculate a relative coarseness value, a relative sparkledensity value, and/or a relative sparkle intensity value of a proposedmatched coating. Additionally, the texture calculation module 130 cancalculate an overall relative texture characteristic value for aproposed, matched coating based upon the set of relative texturecharacteristics determined from the texture variables. For example, theoverall relative texture characteristic value can be directly derivedfrom correlation to human perception, or the overall relative texturecharacteristic value can be calculated from an average of other relativetexture data.

Upon receipt of data from a particular target coating viaspectrophotometer 110 (or other appropriate camera device), the displaymodule 150 can send display instructions for one or more graphics to adisplay unit on computing device 160. The graphics, in turn, displayrelative texture characteristics to a user through a graphical userinterface, such that the user can easily identify the difference intexture characteristics between the target coating (not shown) and eachof the proposed matching coatings (not shown). One will appreciate thatthe displayed characteristics can take on multiple forms. In oneembodiment, the displayed relative texture characteristics may comprisethe single overall texture value, the relative coarseness value, therelative sparkle density value, and/or the relative sparkle intensityvalue for a matched coating. As such, various implementations of thepresent invention can significantly simplify and standardize the textureinformation that is displayed to an end user.

Providing a simple indication of a human-perceived difference betweenone or more coatings can provide significant improvements to thetechnical field of coating matching. In particular, providing aconsistent and standard basis for distinguishing texture attributes of acoating addresses significant shortcomings in the technical art. Assuch, utilizing a statistically standardized approach to utilizinghuman-perception of texture differences can provide an innovative methodfor matching coating textures. For example, in at least oneimplementation, relative texture values can be provided with respect toall available coating compositions, such that it is not necessary toidentify specific potential matching coatings in order to generaterelative texture values. Instead, standardized texture values can becalculated based upon a color and texture space that includes one ormore coating manufacturer's entire coating library.

FIG. 2A depicts an example of a chart 230 derived from gathering andutilizing human-perceived texture differences within implementations ofthe present invention. In particular, FIG. 2A depicts a first examplecoating 200, a second example coating 210 and a human-perspectivetexture comparison chart 230. While the first example coating 200 andthe second example coating 210 are depicted in image form in FIG. 2A,when presented to a user, the first example coating 200 and the secondexample coating 210 can also be actual painted and cured panels. Assuch, the user(s) are provided with true and accurate representations ofthe final coating color and texture.

The human-perspective texture comparison chart 230 is directed towardsdifferences in visual appearance between the first example coating 200and the second example coating 310. For example, the human-perspectivetexture comparison chart 230 requests that a human user indicate whetherthey perceive that the first example coating 200 comprises more or lessoverall perceived texture than the second example coating 210. Asindicated by the human-perspective texture comparison chart 230 of FIG.2A, a human user may be asked to rate the two example coatings 200, 210with regards to a variety of different texture characteristics. Eachrating may be provided using a pre-defined scale of rating options260(a-e).

A large number of users with different racial, gender, and otherdemographic differences can be asked to compare the same two examplecoatings 200, 210 and provide their own respective perceived texturedifferences. The total resulting perceptions of the variety of users canthen be respectively summarized such that a typical, or most-likely,predicted human-perceived texture comparison for each requested texturequestion is calculated.

In the example depicted in FIG. 2A, the user determined that the firstexample coating 200 comprises a “little less” overall perceived texturethan the second example coating 210. Additionally, FIG. 2A shows thatthe user determined that the first example coating 200 comprises“relatively equal” perceived coarseness to the second example coating210. Further, FIG. 2A shows that the user determined that the firstexample coating 200 comprises “a lot less” sparkle intensity than thesecond example coating 210. Further still, FIG. 2A shows that the userdetermined that the first example coating 200 comprises a “little less”sparkle density than the second example coating 210.

FIG. 2B depicts a similar human-perspective texture comparison chart 230to the one depicted in FIG. 2A. In FIG. 2B, however, the chart showsthat the user compares the first example coating 200 to a third examplecoating 220. As indicated by the human-perspective texture comparisonchart 240 of FIG. 2B, the third example coating 220 comprises adifferent texture characteristic profile than either the first examplecoating 200 or the second example coating 210.

FIG. 2C depicts yet another human-perspective texture comparison chart230. In this particular depiction, a user is comparing the third examplecoating 220 to the second example coating 210. Accordingly, FIGS. 2A-2Cillustrate several different results that may be derived fromhuman-perceived comparisons between a variety of different examplecoatings and to provide human-perspective comparisons between theexample coatings across a range of different texture characteristics.The human-perspective comparisons provided to the human-perspectivetexture comparison charts 230 of FIGS. 2A-2C can be translated intorelative numerical values. The resulting values can then be stored in acoating information database 140.

For example, FIG. 3 depicts a number line 330 with indications 300, 310,320 of each respective example coating 200, 210, 220. In particular,FIG. 3 shows that the “X” indication 300 represents the first examplecoating 200, while the square indicator 310 represents the secondexample coating 210, and the circle indicator 320 represents the thirdexample coating 220. The illustrated number line 330 may represent theexamples coatings 200, 210, 220 relative relationships to each otherwith respect to their overall perceived texture. One will understandthat the number line 330 is merely exemplary and is provided for thesake of clarity and example. In practice the relationship betweenvarious texture characteristics of different coatings may comprise a farmore complex, multi-variable relationship that is much more difficult toconveniently depict and describe. Accordingly, the number line 330 isprovided as a simplified example to establish various innovative andnovel features in implementations of the present invention.

The human-perspective texture comparison charts 230, 240, 250 of FIGS.2A-2C comprise five different relative indications 260(a-e). In at leastone implementation, a relative value can be assigned to each indicatorfor each comparison between two respective example coatings, with one ofthe example coatings being considered the “target” from which the otherexample coating is to be compared. For example, the “a lot less thantarget” indicator 260 a may be assigned a relative value of −2, the “alittle less than target” indicator 260 b may be assigned a relativevalue of −1, the “relatively equal to target” indicator 260 c may beassigned a relative value of 0, the “a little more than target”indicator 260 d may be assigned a relative value of +1, and the “a lotmore than target” indicator 260 e may be assigned a relative value of+2. One will understand that the above provided integers of −2, −1, 0,+1, +2 are provided for the sake of example and clarity. Variousimplementations can utilize different schemes, including non-integer andnon-numerical schemes to quantify human-perception.

Returning to the human-perspective texture comparison in FIG. 2A withrespect to “overall perceived texture,” the user indicated that thefirst example coating 200 comprises “a little less” overall perceivedtexture than the second example coating 210. As such, a numerical valueof −1 can be assigned to the first example coating 200 with respect tothe second example coating 210.

In FIG. 2B with respect to “overall perceived texture,” the userindicated that the first example coating 200 comprises “a lot more”overall perceived texture than the third example coating 220. As such, anumerical value of +2 can be assigned to the first example coating 200with respect to the third example coating 220.

In FIG. 2C with respect to “overall perceived texture,” the userindicated that the third example coating 220 comprises “a lot less”overall perceived texture than the second example coating 210. As such,a numerical value of −2 can be assigned to the third example coating 220with respect to the second example coating 210.

An analysis of the above human-perspective texture comparison datareveals that the third example coating 220 comprises “a lot less”overall perceived texture than both the first example coating 200 andthe second example coating 210. This conclusion can be reached basedupon the assumption that the human-perspective texture comparison datain FIG. 2B, which indicates that the first example coating 200 comprises“a lot more” perceived texture than the third example coating 220, isthe equivalent to the third example coating 220 comprising “a lot less”perceived texture than the first example coating 200. A further, similaranalysis of the human-perspective texture comparison data reveals thesecond example coating 210 comprises a little more overall perceivedtexture than the first example coating 200, and a lot more overallperceived texture than the third example coating 220.

These relationships can be depicted by placing the “X” indicator 300 forthe first example coating 200 at “0” on the number line 330. In thisexample, the first example coating 200 is placed at the “0” as a form ofnormalizing the numerical relationships around the medianhuman-perspective texture comparison data point—in this case, the firstexample coating 200. The above data indicated that the second examplecoating 210 was +1 higher in texture than the first example coating 200.This relationship can be represented by placing the square indicator 210for the second example coating 210 on the “+1” on the number line 330.

The placement of the third example coating 220 on the number line 300may comprise accounting for two different human-perspective texturecomparison data points. For example, the human-perspective texturecomparison data indicates that the third example coating 220 comprises“a lot less” overall perceived texture than the second example coating210. Additionally, the human-perspective texture comparison dataindicates that the first example coating 200 comprises “a lot more”overall perceived texture than the third example coating 220. In otherwords, assigning a numerical value to the relationships would requirethat the third example coating 220 be assigned a numerical value of −2with respect to both the first example coating 200 and the secondexample coating 210.

Because the first example coating 200 and the second example coating 210have different overall perceived textures with respect to each other, inat least one implementation, the numerical value of −2 assigned to thethird example coating 220 can be treated as a minimum difference. Assuch, the third example coating 220 can be placed on the number line330, such that it is at least a numerical value of −2 lower than boththe first example coating 200 and the second example coating 210. Thisrelationship is depicted in FIG. 3 by placing the circle indicator 320for the third example coating 220 at the “−2”, while placing the “X”indicator 300 for the first example coating 200 at “0” on the numberline 330, and placing the square indicator 210 for the second examplecoating 210 on the “+1” on the number line 330.

While the number line 330 of FIG. 3 is limited to data from the firstexample coating 200, the second example coating 210, and the thirdexample coating 220, one will appreciate that in at least oneimplementation, the number line 330 can comprise information from acompany's entire coating portfolio or from a large number of randomcoatings. Creating a number line 330 that accounts for a large number ofcoatings may result in a standard basis by which any coating (whetheroriginally accounted for on the number line or not) can be rated. Statedmore generally, comparing a large number of different coatings, andtheir associated textures, can result in a standard comparison metric bywhich textures can be universally compared. The universal standard mayallow a user to enter a single coating into the coating texturecalculation software application 100 and receive an indication of howthe coating's texture compares with respect to the large number ofrandomly entered coating textures. Accordingly, in at least oneimplementation, the coating texture calculation software application 100can provide standardized indicators regarding the texture of aparticular coating, without requiring the user to enter specificcomparison coatings.

FIG. 4 depicts a coating analysis output data table 400 comprisingexample data received from a spectrophotometer 110. FIG. 4 depicts fourexemplary target coating texture variables (λ, δ, σ, and θ) for thefirst example coating 200, the second example coating 210, and the thirdexample coating 220, respectively. As used herein, the target coatingtexture variables λ, δ, σ, and θ, are merely exemplary. Variousdifferent spectrophotometers may provide unique proprietary output data.Similarly, in implementations where the coating texture calculationsoftware application 100 processes images (i.e., photographs) itself, itmay also provide a unique data set of output variables. Accordingly, theexamples provided here with respect to variables λ, δ, σ, and θ aremerely for the sake of clarity and discussion and should not be read tolimit the present invention to any particular method, system, orapparatus.

In at least one implementation, the coating analysis output data table400 and the human-perspective texture comparison charts 230 can bestatistically analyzed with pattern matching algorithms, machinelearning techniques, or otherwise analyzed to identify correlations andpatterns between the various variables within the data table 400 and therelative texture characteristics obtained by human-perspective texturecomparisons. For example, it may be identified that there is an inverserelationship between the difference between λ and δ and the overallperceived texture of a coating. For example, with respect to the thirdexample coating 220, λ is 114 and δ is 21, which results in a differenceof 93. In contrast, the differences between λ and δ for the firstexample coating 210 and the second example coating 200 are 36 and 7,respectively. As such, the third example coating 220 with the leastamount of overall perceived texture comprises the greatest differencebetween λ and δ, while the second example coating with the greatestamount of overall perceived texture comprises the least differencebetween λ and δ.

In at least one implementation, correlations and/or relationships can beidentified between the coating analysis output data table 400, and awide variety of different random coatings. Additionally, the identifiedcorrelations and/or relationships can be used to derive formulasdescribing the identified correlations and/or relationships. As such,the coating texture calculation software application 100 can process anew, unique coating and interpolate various human-perspective texturecharacteristics.

For example, FIG. 5 depicts a graph 500 of the differences between λ andδ for the first, second, and third example coatings, along with theirrespective overall perceived texture. In particular, the graph 500depicts overall perceived texture 520 on the Y-axis and the differencebetween λ and δ 510 on the X-axis. Additionally, using conventionalcurve fitting algorithms or other complex statistical analysis, anequation can be developed that draws a line 530 between each of therespective data points 310, 300, 320.

In at least one implementation, the equation can then be used tointerpolate the overall relative perceived texture for other coatings,based upon the λ and δ variables received from the respective targetcoating. While the equation of FIG. 5 is depicted as being linear andonly depending upon the difference between λ and δ, in at least oneimplementation, the relationship between the received output variablesand a particular perceived texture characteristics may be far morecomplex. As such, the graph 500 and relationship depicted in FIG. 5 isprovided only for the sake of example and clarity. Accordingly,equations, similar to that presented in FIG. 5, can be used tonumerically calculate various relative texture characteristics ofcoatings.

Accordingly, FIGS. 1-5 and the corresponding text depict or otherwisedescribe various implementations of the present invention that areadapted to analyze texture characteristics of a coating. In particular,the present invention can identify how texture characteristics of aparticular coating would be perceived by a human user. One willappreciate that implementations of the present invention can also bedescribed in terms of flowcharts comprising one or more acts foraccomplishing a particular result. For example, FIG. 6 and thecorresponding text describe acts in a method for identifying how texturecharacteristics of a particular coating would be perceived by a humanuser. The acts of FIG. 6 are described below with reference to theelements shown in FIGS. 1-5.

For example, FIG. 6 illustrates that a method for calculating a coatingtextures indicator can include an act 600 of receiving target coatingtexture variables. Act 600 can comprise receiving target coating texturevariables from a spectrophotometer or an image. The target coatingtexture variables can comprise texture data variables generated by thecamera-enabled spectrophotometer or texture data variable calculatedbased upon a received image. For example, as depicted and described withrespect to FIG. 1, a coating texture calculation software application100 (e.g., executed on computing devices 160) can receive from acamera-enabled spectrophotometer 110 various coating texture variables.The received variable may be specific to the device that is used toanalyze the target coating and provide the variables.

Additionally, FIG. 6 shows that the method can include an act 610 ofidentifying a coating color. Act 610 can comprise identifying, basedupon information received from the camera-enabled spectrophotometer, acoating color associated with a target coating. For example, as depictedand described with respect to FIG. 1, a color match module 170 canidentify a coating color based upon information received from acamera-enabled spectrophotometer 110.

FIG. 6 also shows that the method can include an act 620 of accessing arelative texture characteristic database. In at least oneimplementation, the relative texture characteristic database cancomprise a set of relative texture characteristic relationships for oneor more coatings that are related to the coating color. For example, asdepicted and described with respect to FIGS. 3-5, a relative texturecharacteristic relationship can comprise an equation that associatesvarious texture characteristics with texture variables received from animage of a target coating. The relative texture characteristic databasecan comprise a computer storage file of any type that stores relativetexture characteristics of one or more coatings. For example, therelative texture characteristic database may be stored within thecoating information database 140.

In various implementations, multiple equations may exist that describedifferent texture characteristics (e.g., overall perceived texture,overall perceived coarseness, overall perceived sparkle intensity,overall perceived sparkle density, etc.). Additionally, differentequations may need to be developed for different color families. Assuch, in at least one implementation, the equations may be stored withina relative texture characteristic database that maps each equation toits desired input and output. As used herein, a relative texturecharacteristic database can comprise any data structure capable ofstoring at least one correlation between input data and particulartexture characteristics.

In addition, FIG. 6 shows that the method can include an act 630 ofcalculating a correlation between target coating texture variables andcoating texture variables of other coatings. Act 630 can comprisecalculating a correlation between the target coating texture variablesand target coating texture variables associated with the proposedmatched coating. In at least one implementation, the proposed matchedcoating can be identified through a convention color match algorithm.Additionally, in at least one implementation, the proposed matchedcoating is any coating that the target coating is being comparedagainst. For example, as depicted and described with respect to FIG. 5,an equation can be generated (e.g., via a processor at computing devices160 that is executing application 100) that describes a generalrelationship between data received from a spectrophotometer andperceived texture characteristics. The target coating may comprise acoating type that has not previously been analyzed. As such, the inputdata received from the spectrophotometer can be correlated to previousinput data received from previously analyzed coating.

Further, FIG. 6 shows that the method can include an act 640 ofcalculating a set of relative texture characteristics. Act 640 cancomprise based upon the calculated correlation, calculating a set ofrelative texture characteristics for the proposed matched coating thatindicate relative differences in texture between the proposed matchedcoating and the target coating. Each of the relative texturecharacteristics can comprise an assessment over all angles of the targetcoating. For example, as depicted and described with respect to FIGS.2A-5, data received from a spectrophotometer 110 can be entered into anequation that correlates specific input data with perceived texturecharacteristics. The resulting correlation can be used to describe oneor more texture characteristics of a target coating.

Further still, FIG. 6 shows that the method can include an act 650 oftransmitting the relative texture characteristics to a display. Act 650can comprise transmitting digital data capable of causing a display todepict the set of relative texture characteristics. For example, asdepicted and described with respect to FIG. 1, a display module 150 cantransmit resulting data to a display at computing devices 160 160(referred to also as “client computer”). The client computer device 160may comprise a remote computing device or a local computing device. Assuch, in various implementations, the coating texture calculationsoftware 100 can be executed at a remote server, or locally on theclient computer device 160.

Accordingly, implementations of the present invention provide unique andnovel methods and systems for identify perceived texturecharacteristics. In particular, implementations of the present inventioncan map the texture characteristics of a particular target coating to ahuman-perceived texture characteristics based upon previously recordedhuman perceptions regarding other coatings. Implementations of thepresent invention provide significant benefit in the technical field ofcoating texture matching.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above,or the order of the acts described above. Rather, the described featuresand acts are disclosed as example forms of implementing the claims.

Embodiments of the present invention may comprise or utilize aspecial-purpose or general-purpose computer system that includescomputer hardware, such as, for example, one or more processors andsystem memory, as discussed in greater detail below. Embodiments withinthe scope of the present invention also include physical and othercomputer-readable media for carrying or storing computer-executableinstructions and/or data structures. Such computer-readable media can beany available media that can be accessed by a general-purpose orspecial-purpose computer system. Computer-readable media that storecomputer-executable instructions and/or data structures are computerstorage media. Computer-readable media that carry computer-executableinstructions and/or data structures are transmission media. Thus, by wayof example, and not limitation, embodiments of the invention cancomprise at least two distinctly different kinds of computer-readablemedia: computer storage media and transmission media.

Computer storage media are physical storage media that storecomputer-executable instructions and/or data structures. Physicalstorage media include computer hardware, such as RAM, ROM, EEPROM, solidstate drives (“SSDs”), flash memory, phase-change memory (“PCM”),optical disk storage, magnetic disk storage or other magnetic storagedevices, or any other hardware storage device(s) which can be used tostore program code in the form of computer-executable instructions ordata structures, which can be accessed and executed by a general-purposeor special-purpose computer system to implement the disclosedfunctionality of the invention.

Transmission media can include a network and/or data links which can beused to carry program code in the form of computer-executableinstructions or data structures, and which can be accessed by ageneral-purpose or special-purpose computer system. A “network” isdefined as one or more data links that enable the transport ofelectronic data between computer systems and/or modules and/or otherelectronic devices. When information is transferred or provided over anetwork or another communications connection (either hardwired,wireless, or a combination of hardwired or wireless) to a computersystem, the computer system may view the connection as transmissionmedia. Combinations of the above should also be included within thescope of computer-readable media.

Further, upon reaching various computer system components, program codein the form of computer-executable instructions or data structures canbe transferred automatically from transmission media to computer storagemedia (or vice versa). For example, computer-executable instructions ordata structures received over a network or data link can be buffered inRAM within a network interface module (e.g., a “NIC”), and theneventually transferred to computer system RAM and/or to less volatilecomputer storage media at a computer system. Thus, it should beunderstood that computer storage media can be included in computersystem components that also (or even primarily) utilize transmissionmedia.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at one or more processors, cause ageneral-purpose computer system, special-purpose computer system, orspecial-purpose processing device to perform a certain function or groupof functions. Computer-executable instructions may be, for example,binaries, intermediate format instructions such as assembly language, oreven source code.

Those skilled in the art will appreciate that the invention may bepracticed in network computing environments with many types of computersystem configurations, including, personal computers, desktop computers,laptop computers, message processors, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, tablets, pagers, routers, switches, and the like. The inventionmay also be practiced in distributed system environments where local andremote computer systems, which are linked (either by hardwired datalinks, wireless data links, or by a combination of hardwired andwireless data links) through a network, both perform tasks. As such, ina distributed system environment, a computer system may include aplurality of constituent computer systems. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

Those skilled in the art will also appreciate that the invention may bepracticed in a cloud-computing environment. Cloud computing environmentsmay be distributed, although this is not required. When distributed,cloud computing environments may be distributed internationally withinan organization and/or have components possessed across multipleorganizations. In this description and the following claims, “cloudcomputing” is defined as a model for enabling on-demand network accessto a shared pool of configurable computing resources (e.g., networks,servers, storage, applications, and services). The definition of “cloudcomputing” is not limited to any of the other numerous advantages thatcan be obtained from such a model when properly deployed.

A cloud-computing model can be composed of various characteristics, suchas on-demand self-service, broad network access, resource pooling, rapidelasticity, measured service, and so forth. A cloud-computing model mayalso come in the form of various service models such as, for example,Software as a Service (“SaaS”), Platform as a Service (“PaaS”), andInfrastructure as a Service (“IaaS”). The cloud-computing model may alsobe deployed using different deployment models such as private cloud,community cloud, public cloud, hybrid cloud, and so forth.

Some embodiments, such as a cloud-computing environment, may comprise asystem that includes one or more hosts that are each capable of runningone or more virtual machines. During operation, virtual machines emulatean operational computing system, supporting an operating system andperhaps one or more other applications as well. In some embodiments,each host includes a hypervisor that emulates virtual resources for thevirtual machines using physical resources that are abstracted from viewof the virtual machines. The hypervisor also provides proper isolationbetween the virtual machines. Thus, from the perspective of any givenvirtual machine, the hypervisor provides the illusion that the virtualmachine is interfacing with a physical resource, even though the virtualmachine only interfaces with the appearance (e.g., a virtual resource)of a physical resource. Examples of physical resources includingprocessing capacity, memory, disk space, network bandwidth, mediadrives, and so forth.

The present invention therefore relates in particular, without beinglimited thereto, to the following aspects:

1. A computer implemented method comprising: receiving, using at leastone processor, an image of a target coating, determining, using theprocessor, one or more texture variables from the image of the targetcoating, accessing, using the processor, a database comprisingcorresponding texture variables determined for a plurality of referencecoatings and one or more associated relative texture characteristicsobtained by comparative human rating of the visual appearance ofdifferent reference coatings, analyzing, using the processor, the datastored in the database to determine for each of the relative texturecharacteristics a statistical correlation between one or more of thetexture variables and the respective relative texture characteristic;calculating, using the processor, a difference between the determinedone or more texture variables of the target coating and thecorresponding one or more texture variables associated with one or morecoating(s) selected from the reference coatings; calculating, using theprocessor, from the calculated difference in the one or more texturevariables, based upon the determined set of correlations, a set ofrelative texture characteristics for the target coating that indicatesrelative differences in texture of the target coating with respect tothe selected one or more reference coatings; and displaying thecalculated set of relative texture characteristics to a user.

2. The computer implemented method according to aspect 1, wherein eachof the relative texture characteristics corresponds to an assessment ofthe respective coating over all viewing angles.

3. The computer implemented method according to any one of aspect 1 oraspect 2, wherein the image of the target coating, which can be a blackand white image or a color image, is received from a camera-equippedspectrophotometer or from a camera, wherein the camera preferably has anat least three times optical zoom.

4. The computer implemented method according to any one of the precedingaspects further comprising determining, preferably by aspectrophotometer, a color associated with the target coating.

5. The computer implemented method according to any one of the precedingaspects, wherein the one or more coating(s) selected from the referencecoatings are identified by a calculation, using the processor, forfinding a proposed match of the visual appearance or color of the targetcoating from the plurality of reference coatings.

6. The computer implemented method according to any one of the precedingaspects, wherein the relative texture characteristics comprise arelative coarseness, a relative sparkle intensity and/or a relativesparkle density.

7. The computer implemented method according to any one of the precedingaspects further comprising calculating, using the processor, an overallrelative texture value from the set of relative texture characteristicswith respect to the each selected reference coating, and displaying thecalculated overall relative texture value, optionally together with anindication of the associated reference coating, to a user.

8. A system comprising: a user interface comprising a display; adatabase comprising one or more texture variables determined from animage for each of a plurality of reference coatings and one or moreassociated relative texture characteristics obtained by comparativehuman rating of the visual appearance of different reference coatings,at least one processor in communication with the user interface and thedatabase, wherein the at least one processor is configured to: receivean image of a target coating and determine one or more texture variablesfrom the image of the target coating; access the database and analyzethe data stored in the database to determine for each of the relativetexture characteristics a statistical correlation between one or more ofthe texture variables and the respective relative texturecharacteristic; calculate a difference between the determined one ormore texture variables of the target coating and the corresponding oneor more texture variables associated with one or more coating(s)selected from the reference coatings; calculate, using the processor,from the calculated difference in the one or more texture variables,based upon the determined set of correlations, a set of relative texturecharacteristics for the target coating that indicates relativedifferences in texture of the target coating with respect to theselected one or more reference coatings; and display the calculated setof relative texture characteristics on the display to a user.

9. The system according to aspect 8, wherein each of the relativetexture characteristics corresponds to an assessment of the respectivecoating averaged over all viewing angles.

10. The system according to any one of aspect 8 or aspect 9, furthercomprising a camera-equipped spectrophotometer or a camera incommunication with the processor, wherein the camera preferably has anat least three times optical zoom.

11. The system according to any one of the preceding aspects 8-10,wherein the relative texture characteristics comprise a relativecoarseness, a relative sparkle intensity and/or a relative sparkledensity.

12. The system according to any one of the preceding aspects 8-11,wherein the processor is further configured to calculate an overallrelative texture value from the set of relative texture characteristicswith respect to the each selected reference coating, and to display thecalculated overall relative texture value, optionally together with anindication of the associated reference coating, on the display to auser.

13. The system according to any one of the preceding aspects 8-12, beingconfigured to determine, preferably by a spectrophotometer, a colorassociated with the target coating.

14. The system according to any one of the preceding aspects 8-13,wherein the processor is further configured to identify the one or morecoating(s) selected from the reference coatings by a calculation forfinding a proposed match of the visual appearance or color of the targetcoating from the plurality of reference coatings.

15. A non-transitory computer readable medium including software forcausing a processor to: receive an image of a target coating anddetermine one or more texture variables from the image of the targetcoating; access a database comprising corresponding texture variablesdetermined for a plurality of reference coatings and one or moreassociated relative texture characteristics obtained by comparativehuman rating of the visual appearance of different reference coatings;analyze the data stored in the database to determine for each of therelative texture characteristics a statistical correlation between oneor more of the texture variables and the respective relative texturecharacteristic; calculate a difference between the determined one ormore texture variables of the target coating and the corresponding oneor more texture variables associated with one or more coating(s)selected from the reference coatings; calculate from the calculateddifference in the one or more texture variables, based upon thedetermined set of correlations, a set of relative texturecharacteristics for the target coating that indicates relativedifferences in texture of the target coating with respect to theselected one or more reference coatings; and display the calculated setof relative texture characteristics to a user.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the invention is, therefore, indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

I claim:
 1. A computer system for calculating coating texturesindicators, comprising: one or more processors; and one or morecomputer-readable media having stored thereon executable instructionsthat are executable by the one or more processors to configure thecomputer system to perform at least the following: receive targetcoating texture variables from an image of a target coating, wherein thetarget coating texture variables comprise texture data variablesgenerated from the image; access a relative texture characteristicdatabase, wherein the relative texture characteristic database comprisesa set of texture characteristic relationships for a plurality ofcoatings; calculate a correlation between the target coating texturevariables and texture data variables associated with a compared coating;based upon the calculated correlation, calculate a set of relativetexture characteristics for the target coating that indicate relativedifferences in texture between the target coating and the comparedcoating, wherein each of the relative texture characteristics comprisesan assessment over all angles of the target coating; and transmitdigital data that causes a display to depict the set of relative texturecharacteristics.
 2. The computer system of claim 1, the executableinstructions comprising instructions that are executable to configurethe computer system to calculate an overall texture value based upon thecalculated set of relative texture characteristics.
 3. The computersystem of claim 2, the executable instructions comprising instructionsthat are executable to configure the computer system to display on agraphical user interface an indication of the proposed matching coatingand an indication of the overall texture value.
 4. The computer systemof claim 1, the executable instructions comprising instructions that areexecutable to configure the computer system to receive the image from acamera-enabled spectrophotometer.
 5. The computer system of claim 4,wherein the camera-enabled spectrophotometer is configured to generatetexture data based upon a black and white photograph.
 6. The computersystem of claim 4, wherein the camera-enabled spectrophotometer isconfigured to generate texture data based upon a color photograph. 7.The computer system of claim 1, the executable instructions comprisinginstructions that are executable to configure the computer system toreceive the image from a camera.
 8. The computer system of claim 7,wherein the camera comprises an at least three times optical zoom. 9.The computer system of claim 1, wherein the set of relative texturecharacteristics comprises a relative coarseness.
 10. The computer systemof claim 1, wherein the set of relative texture characteristicscomprises a relative sparkle intensity.
 11. The computer system of claim1, wherein the set of relative texture characteristics comprises arelative sparkle density.
 12. The computer system of claim 1, whereinthe set of relative texture characteristics comprises a relativecoarseness, a relative sparkle intensity, and a relative sparkledensity.
 13. The computer system of claim 1, the executable instructionscomprising instructions that are executable to configure the computersystem to: calculate an overall relative texture characteristic value,wherein the overall relative texture characteristic value is calculatedfrom the set of relative texture characteristics; and transmit digitaldata capable of causing a display to depict the overall relative texturecharacteristic value.
 14. The computer system of claim 1, wherein theset of relative texture characteristic relationships are derived fromhuman provided relative impressions between different coatings.
 15. Thecomputer system of claim 14, wherein the relative impressions comprise arelative coarseness, a relative sparkle intensity, and a relativesparkle density with respect to a plurality of different coatings. 16.The computer system of claim 1, the executable instructions comprisinginstructions that are executable to configure the computer system to:identify a proposed matched coating from within the plurality ofcoatings; and based upon the calculated correlation, calculate a set ofrelative texture characteristics for a compared coating that indicaterelative differences in texture between the compared coating and thetarget coating, wherein the compared coating comprises the proposedcoating.
 17. A computer system implemented method that includes one ormore processors, for calculating a coating textures indicator, themethod comprising: receiving target coating texture variables from acamera-enabled spectrophotometer, wherein the target coating texturevariables comprise texture data variables generated by thecamera-enabled spectrophotometer; identifying with a processor, basedupon information received from the camera-enabled spectrophotometer, acoating color associated with a target coating; accessing with aprocessor a relative texture characteristic database, wherein therelative texture characteristic database comprises a set of relativetexture characteristic for one or more coatings that are related to thecoating color; identifying with a processor a proposed matched coatingfrom within the plurality of coatings; calculating with a processor acorrelation between the target coating texture variables and targetcoating texture variables associated with the proposed matched coating;based upon the calculated correlation, calculating with a processor aset of relative texture characteristics for the target coating thatindicate relative differences in texture between the target coating andthe proposed matched coating, wherein each of the relative texturecharacteristics comprises an assessment over all angles of the targetcoating; and transmitting with a processor digital data that causes adisplay to depict the set of relative texture characteristics.
 18. Themethod of claim 17, wherein the set of relative texture characteristicscomprises a relative coarseness, a relative sparkle intensity, and arelative sparkle density.
 19. The method of claim 17, furthercomprising: calculating an overall relative texture characteristicvalue, wherein the overall relative texture characteristic value iscalculated from the set of relative texture characteristics; andtransmitting digital data capable of causing a display to depict theoverall relative texture characteristic value.
 20. A computer systemimplemented method that includes one or more processors, for calculatinga coating textures indicator, the method comprising: receiving targetcoating texture variables from an image of a target coating, wherein thetarget coating texture variables comprise texture data variablesgenerated from the image; accessing a relative texture characteristicdatabase, wherein the relative texture characteristic database comprisesa set of texture characteristic relationships for a plurality ofcoatings; calculating a correlation between the target coating texturevariables and target coating texture variables associated with aplurality of different coatings; based upon the calculated correlation,calculating a set of relative texture characteristics for the targetcoating that indicate relative differences in texture between the targetcoating and the plurality of different coatings, wherein each of therelative texture characteristics comprises an assessment over all anglesof the target coating; transmitting digital data that causes a displayto depict the set of relative texture characteristics.